Overview

Dataset statistics

Number of variables22
Number of observations39607
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.6 MiB
Average record size in memory176.0 B

Variable types

Numeric20
Categorical2

Alerts

X_04 has constant value "1" Constant
X_01 is highly correlated with X_06High correlation
X_03 is highly correlated with X_06High correlation
X_06 is highly correlated with X_01 and 1 other fieldsHigh correlation
Y_01 is highly correlated with Y_02 and 1 other fieldsHigh correlation
Y_02 is highly correlated with Y_01 and 1 other fieldsHigh correlation
Y_03 is highly correlated with Y_01 and 1 other fieldsHigh correlation
Y_04 is highly correlated with Y_05 and 4 other fieldsHigh correlation
Y_05 is highly correlated with Y_04 and 9 other fieldsHigh correlation
Y_06 is highly correlated with Y_04 and 9 other fieldsHigh correlation
Y_07 is highly correlated with Y_04 and 4 other fieldsHigh correlation
Y_08 is highly correlated with Y_05 and 7 other fieldsHigh correlation
Y_09 is highly correlated with Y_05 and 7 other fieldsHigh correlation
Y_10 is highly correlated with Y_04 and 9 other fieldsHigh correlation
Y_11 is highly correlated with Y_04 and 9 other fieldsHigh correlation
Y_12 is highly correlated with Y_05 and 7 other fieldsHigh correlation
Y_13 is highly correlated with Y_05 and 7 other fieldsHigh correlation
Y_14 is highly correlated with Y_05 and 7 other fieldsHigh correlation
X_01 is highly correlated with X_05 and 1 other fieldsHigh correlation
X_05 is highly correlated with X_01High correlation
X_06 is highly correlated with X_01High correlation
Y_01 is highly correlated with Y_02 and 1 other fieldsHigh correlation
Y_02 is highly correlated with Y_01 and 1 other fieldsHigh correlation
Y_03 is highly correlated with Y_01 and 1 other fieldsHigh correlation
Y_04 is highly correlated with Y_05 and 8 other fieldsHigh correlation
Y_05 is highly correlated with Y_04 and 8 other fieldsHigh correlation
Y_06 is highly correlated with Y_10High correlation
Y_07 is highly correlated with Y_04 and 2 other fieldsHigh correlation
Y_08 is highly correlated with Y_04 and 7 other fieldsHigh correlation
Y_09 is highly correlated with Y_04 and 7 other fieldsHigh correlation
Y_10 is highly correlated with Y_04 and 8 other fieldsHigh correlation
Y_11 is highly correlated with Y_04 and 8 other fieldsHigh correlation
Y_12 is highly correlated with Y_04 and 7 other fieldsHigh correlation
Y_13 is highly correlated with Y_04 and 7 other fieldsHigh correlation
Y_14 is highly correlated with Y_04 and 7 other fieldsHigh correlation
Y_01 is highly correlated with Y_02 and 1 other fieldsHigh correlation
Y_02 is highly correlated with Y_01 and 1 other fieldsHigh correlation
Y_03 is highly correlated with Y_01 and 1 other fieldsHigh correlation
Y_04 is highly correlated with Y_05High correlation
Y_05 is highly correlated with Y_04High correlation
Y_06 is highly correlated with Y_08 and 6 other fieldsHigh correlation
Y_08 is highly correlated with Y_06 and 6 other fieldsHigh correlation
Y_09 is highly correlated with Y_06 and 6 other fieldsHigh correlation
Y_10 is highly correlated with Y_06 and 6 other fieldsHigh correlation
Y_11 is highly correlated with Y_06 and 6 other fieldsHigh correlation
Y_12 is highly correlated with Y_06 and 6 other fieldsHigh correlation
Y_13 is highly correlated with Y_06 and 6 other fieldsHigh correlation
Y_14 is highly correlated with Y_06 and 6 other fieldsHigh correlation
X_04 is highly correlated with X_02High correlation
X_02 is highly correlated with X_04High correlation
X_01 is highly correlated with X_05 and 1 other fieldsHigh correlation
X_03 is highly correlated with X_06High correlation
X_05 is highly correlated with X_01High correlation
X_06 is highly correlated with X_01 and 1 other fieldsHigh correlation
Y_01 is highly correlated with Y_02 and 4 other fieldsHigh correlation
Y_02 is highly correlated with Y_01 and 1 other fieldsHigh correlation
Y_03 is highly correlated with Y_01 and 1 other fieldsHigh correlation
Y_04 is highly correlated with Y_05 and 7 other fieldsHigh correlation
Y_05 is highly correlated with Y_04 and 8 other fieldsHigh correlation
Y_06 is highly correlated with Y_01 and 8 other fieldsHigh correlation
Y_07 is highly correlated with Y_01 and 4 other fieldsHigh correlation
Y_08 is highly correlated with Y_04 and 8 other fieldsHigh correlation
Y_09 is highly correlated with Y_04 and 8 other fieldsHigh correlation
Y_10 is highly correlated with Y_01 and 10 other fieldsHigh correlation
Y_11 is highly correlated with Y_04 and 9 other fieldsHigh correlation
Y_12 is highly correlated with Y_04 and 8 other fieldsHigh correlation
Y_13 is highly correlated with Y_04 and 8 other fieldsHigh correlation
Y_14 is highly correlated with Y_04 and 8 other fieldsHigh correlation

Reproduction

Analysis started2022-08-06 09:59:50.086730
Analysis finished2022-08-06 10:00:49.844122
Duration59.76 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

X_01
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct28
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.41203966
Minimum56.268
Maximum84.82
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size309.6 KiB
2022-08-06T19:00:49.923189image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum56.268
5-th percentile64.425
Q166.465
median68.504
Q369.524
95-th percentile73.603
Maximum84.82
Range28.552
Interquartile range (IQR)3.059

Descriptive statistics

Standard deviation2.655982548
Coefficient of variation (CV)0.03882332059
Kurtosis0.7011007556
Mean68.41203966
Median Absolute Deviation (MAD)2.039
Skewness0.4749363865
Sum2709595.655
Variance7.054243296
MonotonicityNot monotonic
2022-08-06T19:00:50.043033image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
68.5046547
16.5%
66.4656324
16.0%
69.5245799
14.6%
67.4855585
14.1%
70.5442886
7.3%
71.5632886
7.3%
65.4452773
7.0%
64.4252300
 
5.8%
72.5831243
 
3.1%
73.6031143
 
2.9%
Other values (18)2121
 
5.4%
ValueCountFrequency (%)
56.2681
 
< 0.1%
58.3076
 
< 0.1%
59.3275
 
< 0.1%
60.34712
 
< 0.1%
61.36686
 
0.2%
62.386211
 
0.5%
63.406765
 
1.9%
64.4252300
 
5.8%
65.4452773
7.0%
66.4656324
16.0%
ValueCountFrequency (%)
84.822
 
< 0.1%
83.83
 
< 0.1%
82.783
 
< 0.1%
81.7614
 
< 0.1%
80.7415
 
< 0.1%
79.72117
 
< 0.1%
78.70230
 
0.1%
77.68248
 
0.1%
76.662140
0.4%
75.642191
0.5%

X_02
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size309.6 KiB
103.32
33020 
103.321
6587 

Length

Max length7
Median length6
Mean length6.166308986
Min length6

Characters and Unicode

Total characters244229
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row103.32
2nd row103.321
3rd row103.32
4th row103.32
5th row103.32

Common Values

ValueCountFrequency (%)
103.3233020
83.4%
103.3216587
 
16.6%

Length

2022-08-06T19:00:50.173393image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-06T19:00:50.296139image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
103.3233020
83.4%
103.3216587
 
16.6%

Most occurring characters

ValueCountFrequency (%)
379214
32.4%
146194
18.9%
039607
16.2%
.39607
16.2%
239607
16.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number204622
83.8%
Other Punctuation39607
 
16.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
379214
38.7%
146194
22.6%
039607
19.4%
239607
19.4%
Other Punctuation
ValueCountFrequency (%)
.39607
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common244229
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
379214
32.4%
146194
18.9%
039607
16.2%
.39607
16.2%
239607
16.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII244229
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
379214
32.4%
146194
18.9%
039607
16.2%
.39607
16.2%
239607
16.2%

X_03
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct280
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.82635367
Minimum56.47
Maximum89.17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size309.6 KiB
2022-08-06T19:00:50.420723image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum56.47
5-th percentile62.77
Q165.07
median67.27
Q371.77
95-th percentile80.17
Maximum89.17
Range32.7
Interquartile range (IQR)6.7

Descriptive statistics

Standard deviation5.15116671
Coefficient of variation (CV)0.07484294074
Kurtosis0.2598532747
Mean68.82635367
Median Absolute Deviation (MAD)2.8
Skewness0.9758827043
Sum2726005.39
Variance26.53451847
MonotonicityNot monotonic
2022-08-06T19:00:50.571552image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65.77509
 
1.3%
66.07504
 
1.3%
65.17495
 
1.2%
65.87492
 
1.2%
66.37489
 
1.2%
65.57488
 
1.2%
65.37481
 
1.2%
66.17481
 
1.2%
65.07480
 
1.2%
65.97469
 
1.2%
Other values (270)34719
87.7%
ValueCountFrequency (%)
56.471
 
< 0.1%
56.771
 
< 0.1%
56.971
 
< 0.1%
57.773
< 0.1%
58.073
< 0.1%
58.572
< 0.1%
58.672
< 0.1%
58.774
< 0.1%
58.873
< 0.1%
58.973
< 0.1%
ValueCountFrequency (%)
89.171
< 0.1%
87.671
< 0.1%
86.872
< 0.1%
86.771
< 0.1%
86.672
< 0.1%
86.571
< 0.1%
85.971
< 0.1%
85.571
< 0.1%
85.471
< 0.1%
85.372
< 0.1%

X_04
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size309.6 KiB
1
39607 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters39607
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
139607
100.0%

Length

2022-08-06T19:00:50.714385image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-06T19:00:50.828903image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
139607
100.0%

Most occurring characters

ValueCountFrequency (%)
139607
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number39607
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
139607
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common39607
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
139607
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII39607
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
139607
100.0%

X_05
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct442
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean102.3372027
Minimum101.774
Maximum103.16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size309.6 KiB
2022-08-06T19:00:50.941794image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum101.774
5-th percentile101.889
Q1101.949
median102.006
Q3103.144
95-th percentile103.157
Maximum103.16
Range1.386
Interquartile range (IQR)1.195

Descriptive statistics

Standard deviation0.548353061
Coefficient of variation (CV)0.005358296362
Kurtosis-1.338285961
Mean102.3372027
Median Absolute Deviation (MAD)0.076
Skewness0.7927655223
Sum4053269.588
Variance0.3006910795
MonotonicityNot monotonic
2022-08-06T19:00:51.090863image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
103.1571276
 
3.2%
103.158990
 
2.5%
103.156889
 
2.2%
103.154820
 
2.1%
103.155791
 
2.0%
103.153757
 
1.9%
103.152714
 
1.8%
103.151619
 
1.6%
103.15567
 
1.4%
103.149446
 
1.1%
Other values (432)31738
80.1%
ValueCountFrequency (%)
101.7741
 
< 0.1%
101.7821
 
< 0.1%
101.7871
 
< 0.1%
101.7881
 
< 0.1%
101.7891
 
< 0.1%
101.793
< 0.1%
101.7912
< 0.1%
101.7921
 
< 0.1%
101.7931
 
< 0.1%
101.7941
 
< 0.1%
ValueCountFrequency (%)
103.16233
 
0.6%
103.159425
 
1.1%
103.158990
2.5%
103.1571276
3.2%
103.156889
2.2%
103.155791
2.0%
103.154820
2.1%
103.153757
1.9%
103.152714
1.8%
103.151619
1.6%

X_06
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct25
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.59721133
Minimum61.726
Maximum87.219
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size309.6 KiB
2022-08-06T19:00:51.230696image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum61.726
5-th percentile66.825
Q168.864
median69.884
Q371.923
95-th percentile73.963
Maximum87.219
Range25.493
Interquartile range (IQR)3.059

Descriptive statistics

Standard deviation2.259819861
Coefficient of variation (CV)0.03201004428
Kurtosis0.691247134
Mean70.59721133
Median Absolute Deviation (MAD)1.02
Skewness0.4373967534
Sum2796143.749
Variance5.106785804
MonotonicityNot monotonic
2022-08-06T19:00:51.352942image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
69.8848476
21.4%
71.9236398
16.2%
68.8646165
15.6%
70.9045486
13.9%
67.8453211
 
8.1%
72.9432838
 
7.2%
73.9632581
 
6.5%
66.8251912
 
4.8%
74.9831206
 
3.0%
65.805392
 
1.0%
Other values (15)942
 
2.4%
ValueCountFrequency (%)
61.7263
 
< 0.1%
62.7463
 
< 0.1%
63.76623
 
0.1%
64.785143
 
0.4%
65.805392
 
1.0%
66.8251912
 
4.8%
67.8453211
 
8.1%
68.8646165
15.6%
69.8848476
21.4%
70.9045486
13.9%
ValueCountFrequency (%)
87.2191
 
< 0.1%
85.182
 
< 0.1%
84.164
 
< 0.1%
83.143
 
< 0.1%
82.1214
 
< 0.1%
81.1017
 
< 0.1%
80.08120
 
0.1%
79.06242
 
0.1%
78.04268
 
0.2%
77.022239
0.6%

X_07
Real number (ℝ≥0)

Distinct1419
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.40748984
Minimum14.14
Maximum163.86
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size309.6 KiB
2022-08-06T19:00:51.496051image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum14.14
5-th percentile26.18
Q127.89
median28.84
Q329.87
95-th percentile32.717
Maximum163.86
Range149.72
Interquartile range (IQR)1.98

Descriptive statistics

Standard deviation7.338203622
Coefficient of variation (CV)0.249535192
Kurtosis301.9674782
Mean29.40748984
Median Absolute Deviation (MAD)0.98
Skewness16.73832528
Sum1164742.45
Variance53.84923241
MonotonicityNot monotonic
2022-08-06T19:00:51.645004image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28.82144
 
0.4%
28.86142
 
0.4%
28.39139
 
0.4%
28.71132
 
0.3%
28.92132
 
0.3%
28.76131
 
0.3%
28.38131
 
0.3%
29.06131
 
0.3%
28.74130
 
0.3%
28.64129
 
0.3%
Other values (1409)38266
96.6%
ValueCountFrequency (%)
14.141
< 0.1%
23.251
< 0.1%
23.461
< 0.1%
23.921
< 0.1%
23.961
< 0.1%
23.971
< 0.1%
23.991
< 0.1%
241
< 0.1%
24.041
< 0.1%
24.061
< 0.1%
ValueCountFrequency (%)
163.8694
0.2%
163.851
 
< 0.1%
163.811
 
< 0.1%
163.781
 
< 0.1%
163.771
 
< 0.1%
163.652
 
< 0.1%
163.641
 
< 0.1%
163.571
 
< 0.1%
163.561
 
< 0.1%
163.521
 
< 0.1%

X_08
Real number (ℝ≥0)

Distinct13659
Distinct (%)34.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean164.4493196
Minimum38.46
Maximum2387.44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size309.6 KiB
2022-08-06T19:00:51.803823image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum38.46
5-th percentile74.1
Q1105.99
median115.04
Q3132.62
95-th percentile328.509
Maximum2387.44
Range2348.98
Interquartile range (IQR)26.63

Descriptive statistics

Standard deviation220.4024435
Coefficient of variation (CV)1.340245397
Kurtosis50.36262653
Mean164.4493196
Median Absolute Deviation (MAD)10.9
Skewness6.649749706
Sum6513344.2
Variance48577.23712
MonotonicityNot monotonic
2022-08-06T19:00:51.956251image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
113.9730
 
0.1%
116.1128
 
0.1%
112.2826
 
0.1%
117.0426
 
0.1%
114.4526
 
0.1%
115.5325
 
0.1%
114.1725
 
0.1%
117.4825
 
0.1%
115.6625
 
0.1%
114.4124
 
0.1%
Other values (13649)39347
99.3%
ValueCountFrequency (%)
38.461
< 0.1%
42.41
< 0.1%
42.531
< 0.1%
42.641
< 0.1%
42.891
< 0.1%
42.921
< 0.1%
43.031
< 0.1%
43.231
< 0.1%
43.41
< 0.1%
43.461
< 0.1%
ValueCountFrequency (%)
2387.445
< 0.1%
2387.431
 
< 0.1%
2387.424
< 0.1%
2387.411
 
< 0.1%
2387.381
 
< 0.1%
2387.362
 
< 0.1%
2387.331
 
< 0.1%
2387.31
 
< 0.1%
2387.261
 
< 0.1%
2387.241
 
< 0.1%

Y_01
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2249
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.353813796
Minimum0.017
Maximum4.409
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size309.6 KiB
2022-08-06T19:00:52.109626image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.017
5-th percentile0.7833
Q11.1275
median1.349
Q31.576
95-th percentile1.931
Maximum4.409
Range4.392
Interquartile range (IQR)0.4485

Descriptive statistics

Standard deviation0.3562231101
Coefficient of variation (CV)0.2631256316
Kurtosis1.210970899
Mean1.353813796
Median Absolute Deviation (MAD)0.224
Skewness0.1502434869
Sum53620.503
Variance0.1268949041
MonotonicityNot monotonic
2022-08-06T19:00:52.258004image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.37664
 
0.2%
1.31262
 
0.2%
1.33360
 
0.2%
1.4260
 
0.2%
1.38960
 
0.2%
1.27860
 
0.2%
1.360
 
0.2%
1.30859
 
0.1%
1.458
 
0.1%
1.26358
 
0.1%
Other values (2239)39006
98.5%
ValueCountFrequency (%)
0.0171
 
< 0.1%
0.0181
 
< 0.1%
0.0192
< 0.1%
0.023
< 0.1%
0.0212
< 0.1%
0.0252
< 0.1%
0.0262
< 0.1%
0.0271
 
< 0.1%
0.0281
 
< 0.1%
0.0351
 
< 0.1%
ValueCountFrequency (%)
4.4091
< 0.1%
4.0811
< 0.1%
3.791
< 0.1%
3.721
< 0.1%
3.5291
< 0.1%
3.5181
< 0.1%
3.5011
< 0.1%
3.4991
< 0.1%
3.4191
< 0.1%
3.3641
< 0.1%

Y_02
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2227
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.057267251
Minimum0.007
Maximum3.998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size309.6 KiB
2022-08-06T19:00:52.414191image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.007
5-th percentile0.45
Q10.793
median1.044
Q31.3
95-th percentile1.711
Maximum3.998
Range3.991
Interquartile range (IQR)0.507

Descriptive statistics

Standard deviation0.386265985
Coefficient of variation (CV)0.3653437527
Kurtosis0.6736418075
Mean1.057267251
Median Absolute Deviation (MAD)0.254
Skewness0.3657652688
Sum41875.184
Variance0.1492014112
MonotonicityNot monotonic
2022-08-06T19:00:52.579933image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.07259
 
0.1%
0.81459
 
0.1%
1.1258
 
0.1%
1.04357
 
0.1%
0.92456
 
0.1%
0.88856
 
0.1%
1.03155
 
0.1%
1.14455
 
0.1%
0.90255
 
0.1%
0.83454
 
0.1%
Other values (2217)39043
98.6%
ValueCountFrequency (%)
0.0072
 
< 0.1%
0.0084
< 0.1%
0.0093
< 0.1%
0.014
< 0.1%
0.0113
< 0.1%
0.0123
< 0.1%
0.0135
< 0.1%
0.0142
 
< 0.1%
0.0157
< 0.1%
0.0166
< 0.1%
ValueCountFrequency (%)
3.9981
< 0.1%
3.971
< 0.1%
3.721
< 0.1%
3.5521
< 0.1%
3.2881
< 0.1%
3.2561
< 0.1%
3.2281
< 0.1%
3.1421
< 0.1%
3.1151
< 0.1%
3.0491
< 0.1%

Y_03
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2127
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.014001717
Minimum0.017
Maximum3.756
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size309.6 KiB
2022-08-06T19:00:52.746422image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.017
5-th percentile0.451
Q10.769
median0.998
Q31.239
95-th percentile1.628
Maximum3.756
Range3.739
Interquartile range (IQR)0.47

Descriptive statistics

Standard deviation0.3614919509
Coefficient of variation (CV)0.3565003341
Kurtosis0.7764764849
Mean1.014001717
Median Absolute Deviation (MAD)0.235
Skewness0.396399124
Sum40161.566
Variance0.1306764305
MonotonicityNot monotonic
2022-08-06T19:00:52.897102image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.88866
 
0.2%
0.98863
 
0.2%
0.97362
 
0.2%
0.97161
 
0.2%
0.96561
 
0.2%
1.10460
 
0.2%
0.86960
 
0.2%
0.99959
 
0.1%
0.90657
 
0.1%
0.84657
 
0.1%
Other values (2117)39001
98.5%
ValueCountFrequency (%)
0.0171
 
< 0.1%
0.0191
 
< 0.1%
0.0214
< 0.1%
0.0221
 
< 0.1%
0.0242
< 0.1%
0.0252
< 0.1%
0.0272
< 0.1%
0.0293
< 0.1%
0.032
< 0.1%
0.0311
 
< 0.1%
ValueCountFrequency (%)
3.7561
< 0.1%
3.7131
< 0.1%
3.2841
< 0.1%
3.2131
< 0.1%
3.1981
< 0.1%
3.1821
< 0.1%
3.1021
< 0.1%
3.0991
< 0.1%
3.0691
< 0.1%
3.0281
< 0.1%

Y_04
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct10773
Distinct (%)27.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.62119133
Minimum-0.331
Maximum98.794
Zeros0
Zeros (%)0.0%
Negative3
Negative (%)< 0.1%
Memory size309.6 KiB
2022-08-06T19:00:53.061213image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-0.331
5-th percentile8.9393
Q111.822
median13.837
Q315.626
95-th percentile17.5587
Maximum98.794
Range99.125
Interquartile range (IQR)3.804

Descriptive statistics

Standard deviation2.686631665
Coefficient of variation (CV)0.1972391107
Kurtosis25.18483477
Mean13.62119133
Median Absolute Deviation (MAD)1.887
Skewness0.4534505598
Sum539494.525
Variance7.217989702
MonotonicityNot monotonic
2022-08-06T19:00:53.212069image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.85215
 
< 0.1%
14.24314
 
< 0.1%
13.34914
 
< 0.1%
15.70713
 
< 0.1%
15.46113
 
< 0.1%
13.8913
 
< 0.1%
14.40913
 
< 0.1%
14.77413
 
< 0.1%
15.06313
 
< 0.1%
15.43613
 
< 0.1%
Other values (10763)39473
99.7%
ValueCountFrequency (%)
-0.3311
< 0.1%
-0.3271
< 0.1%
-0.3081
< 0.1%
2.2421
< 0.1%
3.3121
< 0.1%
3.4471
< 0.1%
3.4781
< 0.1%
3.8331
< 0.1%
3.8471
< 0.1%
3.9241
< 0.1%
ValueCountFrequency (%)
98.7941
< 0.1%
33.3331
< 0.1%
25.9561
< 0.1%
21.4621
< 0.1%
21.4421
< 0.1%
20.891
< 0.1%
20.4761
< 0.1%
20.3211
< 0.1%
20.2091
< 0.1%
20.2041
< 0.1%

Y_05
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct10241
Distinct (%)25.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.29046706
Minimum18.589
Maximum37.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size309.6 KiB
2022-08-06T19:00:53.366722image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum18.589
5-th percentile26.512
Q129.768
median31.71
Q333.184
95-th percentile34.709
Maximum37.25
Range18.661
Interquartile range (IQR)3.416

Descriptive statistics

Standard deviation2.543221628
Coefficient of variation (CV)0.08127784168
Kurtosis0.489914823
Mean31.29046706
Median Absolute Deviation (MAD)1.658
Skewness-0.7720326285
Sum1239321.529
Variance6.467976249
MonotonicityNot monotonic
2022-08-06T19:00:53.517778image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32.69218
 
< 0.1%
33.46517
 
< 0.1%
32.49116
 
< 0.1%
31.94915
 
< 0.1%
33.21515
 
< 0.1%
31.71315
 
< 0.1%
32.65915
 
< 0.1%
32.59315
 
< 0.1%
32.715
 
< 0.1%
32.79615
 
< 0.1%
Other values (10231)39451
99.6%
ValueCountFrequency (%)
18.5891
< 0.1%
19.3951
< 0.1%
19.7041
< 0.1%
20.0621
< 0.1%
20.0672
< 0.1%
20.1231
< 0.1%
20.1891
< 0.1%
20.241
< 0.1%
20.4171
< 0.1%
20.4621
< 0.1%
ValueCountFrequency (%)
37.251
< 0.1%
37.2251
< 0.1%
37.1012
< 0.1%
36.9951
< 0.1%
36.9791
< 0.1%
36.9151
< 0.1%
36.8681
< 0.1%
36.8371
< 0.1%
36.8081
< 0.1%
36.8061
< 0.1%

Y_06
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4269
Distinct (%)10.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.52938208
Minimum-19.963
Maximum18.998
Zeros0
Zeros (%)0.0%
Negative99
Negative (%)0.2%
Memory size309.6 KiB
2022-08-06T19:00:53.681361image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-19.963
5-th percentile15.177
Q116.146
median16.694
Q317.164
95-th percentile17.758
Maximum18.998
Range38.961
Interquartile range (IQR)1.018

Descriptive statistics

Standard deviation1.89301384
Coefficient of variation (CV)0.1145241747
Kurtosis270.339787
Mean16.52938208
Median Absolute Deviation (MAD)0.501
Skewness-15.02970347
Sum654679.236
Variance3.583501399
MonotonicityNot monotonic
2022-08-06T19:00:53.842389image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.78238
 
0.1%
16.87236
 
0.1%
17.09933
 
0.1%
16.85932
 
0.1%
17.13832
 
0.1%
16.96732
 
0.1%
16.7232
 
0.1%
16.84731
 
0.1%
16.7631
 
0.1%
16.47531
 
0.1%
Other values (4259)39279
99.2%
ValueCountFrequency (%)
-19.9631
< 0.1%
-19.6021
< 0.1%
-19.5171
< 0.1%
-19.4721
< 0.1%
-19.4431
< 0.1%
-19.3671
< 0.1%
-19.3511
< 0.1%
-19.2521
< 0.1%
-19.232
< 0.1%
-19.0991
< 0.1%
ValueCountFrequency (%)
18.9981
< 0.1%
18.9921
< 0.1%
18.8881
< 0.1%
18.8571
< 0.1%
18.8241
< 0.1%
18.7861
< 0.1%
18.7531
< 0.1%
18.7281
< 0.1%
18.6921
< 0.1%
18.6851
< 0.1%

Y_07
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2394
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.155054107
Minimum0.502
Maximum5.299
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size309.6 KiB
2022-08-06T19:00:54.003270image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.502
5-th percentile2.53
Q12.863
median3.126
Q33.4335
95-th percentile3.864
Maximum5.299
Range4.797
Interquartile range (IQR)0.5705

Descriptive statistics

Standard deviation0.4189399013
Coefficient of variation (CV)0.1327837454
Kurtosis0.7671083874
Mean3.155054107
Median Absolute Deviation (MAD)0.283
Skewness0.08450194006
Sum124962.228
Variance0.1755106409
MonotonicityNot monotonic
2022-08-06T19:00:54.157933image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.00557
 
0.1%
2.9456
 
0.1%
3.13653
 
0.1%
2.98852
 
0.1%
3.12852
 
0.1%
3.03351
 
0.1%
3.27251
 
0.1%
2.97651
 
0.1%
3.0351
 
0.1%
3.0850
 
0.1%
Other values (2384)39083
98.7%
ValueCountFrequency (%)
0.5021
< 0.1%
0.6851
< 0.1%
0.7231
< 0.1%
0.8181
< 0.1%
0.8791
< 0.1%
0.9111
< 0.1%
0.9211
< 0.1%
0.9331
< 0.1%
0.9451
< 0.1%
0.9531
< 0.1%
ValueCountFrequency (%)
5.2991
< 0.1%
5.1181
< 0.1%
4.9991
< 0.1%
4.9911
< 0.1%
4.9821
< 0.1%
4.9271
< 0.1%
4.9181
< 0.1%
4.911
< 0.1%
4.8681
< 0.1%
4.851
< 0.1%

Y_08
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3672
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-26.29483879
Minimum-29.652
Maximum-23.785
Zeros0
Zeros (%)0.0%
Negative39607
Negative (%)100.0%
Memory size309.6 KiB
2022-08-06T19:00:54.309606image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-29.652
5-th percentile-27.4447
Q1-26.689
median-26.254
Q3-25.855
95-th percentile-25.283
Maximum-23.785
Range5.867
Interquartile range (IQR)0.834

Descriptive statistics

Standard deviation0.6605368289
Coefficient of variation (CV)-0.0251203985
Kurtosis0.7493218708
Mean-26.29483879
Median Absolute Deviation (MAD)0.415
Skewness-0.4373902743
Sum-1041459.68
Variance0.4363089024
MonotonicityNot monotonic
2022-08-06T19:00:54.455943image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-26.31441
 
0.1%
-26.09140
 
0.1%
-25.83840
 
0.1%
-26.43139
 
0.1%
-26.43539
 
0.1%
-26.0939
 
0.1%
-26.08138
 
0.1%
-26.37238
 
0.1%
-26.04537
 
0.1%
-26.12236
 
0.1%
Other values (3662)39220
99.0%
ValueCountFrequency (%)
-29.6521
< 0.1%
-29.6421
< 0.1%
-29.6051
< 0.1%
-29.5781
< 0.1%
-29.4521
< 0.1%
-29.3521
< 0.1%
-29.331
< 0.1%
-29.3241
< 0.1%
-29.3092
< 0.1%
-29.3061
< 0.1%
ValueCountFrequency (%)
-23.7851
< 0.1%
-24.0131
< 0.1%
-24.1171
< 0.1%
-24.1421
< 0.1%
-24.1581
< 0.1%
-24.1621
< 0.1%
-24.181
< 0.1%
-24.191
< 0.1%
-24.2071
< 0.1%
-24.2111
< 0.1%

Y_09
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3649
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-26.30862254
Minimum-29.523
Maximum-23.96
Zeros0
Zeros (%)0.0%
Negative39607
Negative (%)100.0%
Memory size309.6 KiB
2022-08-06T19:00:54.611615image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-29.523
5-th percentile-27.44
Q1-26.702
median-26.266
Q3-25.871
95-th percentile-25.311
Maximum-23.96
Range5.563
Interquartile range (IQR)0.831

Descriptive statistics

Standard deviation0.6535798156
Coefficient of variation (CV)-0.02484279877
Kurtosis0.7264309573
Mean-26.30862254
Median Absolute Deviation (MAD)0.414
Skewness-0.4318247115
Sum-1042005.613
Variance0.4271665753
MonotonicityNot monotonic
2022-08-06T19:00:54.756793image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-26.3143
 
0.1%
-26.22841
 
0.1%
-26.2838
 
0.1%
-26.10638
 
0.1%
-26.26537
 
0.1%
-26.12337
 
0.1%
-26.0437
 
0.1%
-26.37537
 
0.1%
-26.1137
 
0.1%
-26.33136
 
0.1%
Other values (3639)39226
99.0%
ValueCountFrequency (%)
-29.5231
< 0.1%
-29.4771
< 0.1%
-29.471
< 0.1%
-29.4271
< 0.1%
-29.3921
< 0.1%
-29.3761
< 0.1%
-29.3511
< 0.1%
-29.3391
< 0.1%
-29.3381
< 0.1%
-29.3311
< 0.1%
ValueCountFrequency (%)
-23.961
< 0.1%
-23.9851
< 0.1%
-24.0911
< 0.1%
-24.1041
< 0.1%
-24.1551
< 0.1%
-24.161
< 0.1%
-24.1891
< 0.1%
-24.2191
< 0.1%
-24.2421
< 0.1%
-24.2761
< 0.1%

Y_10
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4458
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-22.40006244
Minimum-31.119
Maximum-20.052
Zeros0
Zeros (%)0.0%
Negative39607
Negative (%)100.0%
Memory size309.6 KiB
2022-08-06T19:00:54.917280image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-31.119
5-th percentile-23.9517
Q1-22.871
median-22.275
Q3-21.791
95-th percentile-21.195
Maximum-20.052
Range11.067
Interquartile range (IQR)1.08

Descriptive statistics

Standard deviation0.920952195
Coefficient of variation (CV)-0.04111382268
Kurtosis10.34745855
Mean-22.40006244
Median Absolute Deviation (MAD)0.529
Skewness-1.837054602
Sum-887199.273
Variance0.8481529455
MonotonicityNot monotonic
2022-08-06T19:00:55.628815image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-22.01433
 
0.1%
-21.99533
 
0.1%
-21.91932
 
0.1%
-22.09632
 
0.1%
-22.32732
 
0.1%
-22.17631
 
0.1%
-22.09231
 
0.1%
-22.32931
 
0.1%
-22.06531
 
0.1%
-21.78630
 
0.1%
Other values (4448)39291
99.2%
ValueCountFrequency (%)
-31.1191
< 0.1%
-30.9491
< 0.1%
-30.9261
< 0.1%
-30.7881
< 0.1%
-30.6191
< 0.1%
-30.5871
< 0.1%
-30.5841
< 0.1%
-30.5481
< 0.1%
-30.5371
< 0.1%
-30.5071
< 0.1%
ValueCountFrequency (%)
-20.0521
 
< 0.1%
-20.0931
 
< 0.1%
-20.131
 
< 0.1%
-20.1471
 
< 0.1%
-20.2241
 
< 0.1%
-20.2351
 
< 0.1%
-20.2721
 
< 0.1%
-20.2883
< 0.1%
-20.311
 
< 0.1%
-20.3311
 
< 0.1%

Y_11
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4309
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.32506113
Minimum19.844
Maximum26.703
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size309.6 KiB
2022-08-06T19:00:55.782999image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum19.844
5-th percentile22.815
Q123.836
median24.42
Q324.9115
95-th percentile25.514
Maximum26.703
Range6.859
Interquartile range (IQR)1.0755

Descriptive statistics

Standard deviation0.8301968024
Coefficient of variation (CV)0.03412927919
Kurtosis0.7579205164
Mean24.32506113
Median Absolute Deviation (MAD)0.532
Skewness-0.6749349242
Sum963442.696
Variance0.6892267307
MonotonicityNot monotonic
2022-08-06T19:00:55.934710image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.73734
 
0.1%
24.77634
 
0.1%
24.49633
 
0.1%
24.5832
 
0.1%
24.54232
 
0.1%
24.40932
 
0.1%
24.64432
 
0.1%
24.50931
 
0.1%
24.74131
 
0.1%
24.58831
 
0.1%
Other values (4299)39285
99.2%
ValueCountFrequency (%)
19.8441
< 0.1%
20.0311
< 0.1%
20.0451
< 0.1%
20.1011
< 0.1%
20.1751
< 0.1%
20.1941
< 0.1%
20.1991
< 0.1%
20.2951
< 0.1%
20.2981
< 0.1%
20.3341
< 0.1%
ValueCountFrequency (%)
26.7031
< 0.1%
26.6591
< 0.1%
26.6571
< 0.1%
26.5921
< 0.1%
26.5791
< 0.1%
26.5671
< 0.1%
26.5511
< 0.1%
26.5451
< 0.1%
26.4831
< 0.1%
26.481
< 0.1%

Y_12
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3673
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-26.23776173
Minimum-29.544
Maximum-23.722
Zeros0
Zeros (%)0.0%
Negative39607
Negative (%)100.0%
Memory size309.6 KiB
2022-08-06T19:00:56.096280image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-29.544
5-th percentile-27.38
Q1-26.63
median-26.198
Q3-25.799
95-th percentile-25.238
Maximum-23.722
Range5.822
Interquartile range (IQR)0.831

Descriptive statistics

Standard deviation0.6563285123
Coefficient of variation (CV)-0.02501465327
Kurtosis0.7459825
Mean-26.23776173
Median Absolute Deviation (MAD)0.413
Skewness-0.4446574078
Sum-1039199.029
Variance0.430767116
MonotonicityNot monotonic
2022-08-06T19:00:56.243501image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-26.02649
 
0.1%
-26.1741
 
0.1%
-26.07640
 
0.1%
-26.35839
 
0.1%
-26.11839
 
0.1%
-26.4438
 
0.1%
-26.29238
 
0.1%
-25.99837
 
0.1%
-25.91937
 
0.1%
-26.35437
 
0.1%
Other values (3663)39212
99.0%
ValueCountFrequency (%)
-29.5441
< 0.1%
-29.4531
< 0.1%
-29.4411
< 0.1%
-29.3671
< 0.1%
-29.3461
< 0.1%
-29.3411
< 0.1%
-29.3351
< 0.1%
-29.311
< 0.1%
-29.2871
< 0.1%
-29.2831
< 0.1%
ValueCountFrequency (%)
-23.7221
< 0.1%
-23.9471
< 0.1%
-23.951
< 0.1%
-24.0671
< 0.1%
-24.1511
< 0.1%
-24.161
< 0.1%
-24.2211
< 0.1%
-24.2281
< 0.1%
-24.2311
< 0.1%
-24.241
< 0.1%

Y_13
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3665
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-26.23386934
Minimum-29.448
Maximum-23.899
Zeros0
Zeros (%)0.0%
Negative39607
Negative (%)100.0%
Memory size309.6 KiB
2022-08-06T19:00:56.400058image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-29.448
5-th percentile-27.366
Q1-26.624
median-26.193
Q3-25.794
95-th percentile-25.2393
Maximum-23.899
Range5.549
Interquartile range (IQR)0.83

Descriptive statistics

Standard deviation0.6550900257
Coefficient of variation (CV)-0.02497115531
Kurtosis0.7518019689
Mean-26.23386934
Median Absolute Deviation (MAD)0.413
Skewness-0.4398630698
Sum-1039044.863
Variance0.4291429417
MonotonicityNot monotonic
2022-08-06T19:00:56.547902image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-26.09742
 
0.1%
-26.18741
 
0.1%
-26.33641
 
0.1%
-26.2840
 
0.1%
-26.15139
 
0.1%
-26.34638
 
0.1%
-26.03938
 
0.1%
-25.96837
 
0.1%
-26.21336
 
0.1%
-25.99536
 
0.1%
Other values (3655)39219
99.0%
ValueCountFrequency (%)
-29.4481
< 0.1%
-29.4431
< 0.1%
-29.3751
< 0.1%
-29.3681
< 0.1%
-29.3551
< 0.1%
-29.351
< 0.1%
-29.3011
< 0.1%
-29.2921
< 0.1%
-29.2361
< 0.1%
-29.2261
< 0.1%
ValueCountFrequency (%)
-23.8991
< 0.1%
-23.9361
< 0.1%
-23.9651
< 0.1%
-24.0211
< 0.1%
-24.1171
< 0.1%
-24.1231
< 0.1%
-24.1771
< 0.1%
-24.1941
< 0.1%
-24.2051
< 0.1%
-24.211
< 0.1%

Y_14
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3682
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-26.24586843
Minimum-29.62
Maximum-23.856
Zeros0
Zeros (%)0.0%
Negative39607
Negative (%)100.0%
Memory size309.6 KiB
2022-08-06T19:00:56.705025image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-29.62
5-th percentile-27.3817
Q1-26.64
median-26.204
Q3-25.809
95-th percentile-25.245
Maximum-23.856
Range5.764
Interquartile range (IQR)0.831

Descriptive statistics

Standard deviation0.6559887312
Coefficient of variation (CV)-0.02499398078
Kurtosis0.734812393
Mean-26.24586843
Median Absolute Deviation (MAD)0.413
Skewness-0.4307872388
Sum-1039520.111
Variance0.4303212155
MonotonicityNot monotonic
2022-08-06T19:00:56.860590image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-26.30346
 
0.1%
-26.02541
 
0.1%
-26.14839
 
0.1%
-25.85839
 
0.1%
-26.0839
 
0.1%
-26.17438
 
0.1%
-26.10538
 
0.1%
-25.83538
 
0.1%
-26.42437
 
0.1%
-26.06537
 
0.1%
Other values (3672)39215
99.0%
ValueCountFrequency (%)
-29.621
< 0.1%
-29.5291
< 0.1%
-29.4931
< 0.1%
-29.4341
< 0.1%
-29.341
< 0.1%
-29.3351
< 0.1%
-29.3121
< 0.1%
-29.2921
< 0.1%
-29.2821
< 0.1%
-29.281
< 0.1%
ValueCountFrequency (%)
-23.8561
< 0.1%
-24.0521
< 0.1%
-24.1372
< 0.1%
-24.1391
< 0.1%
-24.1651
< 0.1%
-24.1761
< 0.1%
-24.1921
< 0.1%
-24.1931
< 0.1%
-24.2081
< 0.1%
-24.2111
< 0.1%

Interactions

2022-08-06T19:00:46.415826image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T18:59:54.179799image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T18:59:56.826360image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:00.015847image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:02.817499image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:05.387447image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:08.116963image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:11.034549image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:13.586856image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:16.388626image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:19.115620image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:21.705809image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:24.767469image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:27.530307image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:30.091765image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:32.727724image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:35.375732image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:38.028101image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:41.163802image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:43.794064image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:46.561916image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T18:59:54.312060image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T18:59:56.961096image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:00.159681image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:02.945759image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:05.522057image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:08.249772image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:11.161046image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:13.725650image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:16.526121image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:19.244146image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:21.842462image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:24.902858image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:27.657966image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:30.223003image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:32.860369image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:35.506155image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:38.584571image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:41.296019image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:43.925751image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:46.696526image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T18:59:54.448474image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T18:59:57.100754image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:00.307894image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:03.076374image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:05.661698image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:08.383419image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:11.292859image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:13.870260image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:16.672856image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:19.377826image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:21.980981image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:25.050587image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:27.789641image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:30.358793image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:32.996699image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:35.642619image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:38.722508image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:41.430235image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:44.060449image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:46.834187image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T18:59:54.587449image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T18:59:57.244324image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:00.455834image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:03.209450image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:05.803812image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:08.519803image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:11.426904image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:14.016781image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:16.822104image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:19.514460image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:22.122668image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:25.197824image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:27.924267image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:30.495847image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:33.137322image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:35.780313image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:38.866124image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:41.568481image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:44.198739image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:46.956830image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T18:59:54.711439image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T18:59:57.372492image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:00.586485image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:03.330099image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:05.929969image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:08.641737image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:11.545912image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:14.151227image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:16.952011image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:19.636188image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:22.251494image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:25.327660image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:28.041982image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:30.619987image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:33.260752image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:35.919349image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:38.994780image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:41.694651image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:44.323096image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:47.091470image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T18:59:54.846946image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T18:59:57.511468image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:00.729103image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:03.463768image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:06.069566image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:08.774911image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:11.675792image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:14.295841image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:17.092921image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:19.768833image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:22.391111image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:25.468379image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:28.172565image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:30.757091image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:33.397359image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:36.053156image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:39.134407image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:41.834983image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:44.457407image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:47.218130image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T18:59:54.974321image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-08-06T19:00:02.249363image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:04.875088image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:07.579315image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:10.509923image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:13.073746image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:15.830801image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:18.576140image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:21.187651image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:24.227221image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:26.959441image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:29.573349image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:32.202966image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:34.833282image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:37.507188image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:40.611800image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:43.275392image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:45.893030image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:48.666580image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T18:59:56.429174image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T18:59:59.150077image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:02.394652image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:05.008874image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:07.718916image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:10.646838image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:13.207086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:15.975207image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:18.715212image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:21.321096image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:24.367492image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:27.130046image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:29.704864image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:32.337616image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:34.969117image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:37.642734image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:40.753400image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:43.408709image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:46.029310image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:48.793277image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T18:59:56.562550image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T18:59:59.282788image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:02.533963image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:05.136050image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:07.850588image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:10.776244image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:13.334373image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:16.109835image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:18.850956image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:21.451019image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:24.500891image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:27.263968image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:29.832063image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:32.465926image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:35.099794image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:37.772272image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:40.894495image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:43.536413image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:46.157119image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:48.922866image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T18:59:56.695685image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T18:59:59.414049image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:02.676384image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:05.263022image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:07.983209image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:10.905727image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:13.459809image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:16.246685image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:18.983855image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:21.577807image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:24.634329image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:27.397784image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:29.957621image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:32.596239image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:35.245972image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:37.900567image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:41.029402image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:43.664011image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-06T19:00:46.283742image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-08-06T19:00:57.021554image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-08-06T19:00:57.274532image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-08-06T19:00:57.528846image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-08-06T19:00:57.749256image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-08-06T19:00:57.881728image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-08-06T19:00:49.146584image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-08-06T19:00:49.626686image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

X_01X_02X_03X_04X_05X_06X_07X_08Y_01Y_02Y_03Y_04Y_05Y_06Y_07Y_08Y_09Y_10Y_11Y_12Y_13Y_14
070.544103.32067.471101.89274.98329.4562.382.0561.4561.68010.50229.63216.0834.276-25.381-25.529-22.76923.792-25.470-25.409-25.304
169.524103.32165.171101.94472.94328.7361.231.4461.1841.26818.50733.17916.7363.229-26.619-26.523-22.57424.691-26.253-26.497-26.438
272.583103.32064.071103.15372.94328.81105.771.2510.6650.78214.08231.80117.0802.839-26.238-26.216-22.16924.649-26.285-26.215-26.370
371.563103.32067.571101.97177.02228.92115.211.4641.0791.05216.97534.50317.1433.144-25.426-25.079-21.76524.913-25.254-25.021-25.345
469.524103.32063.571101.98170.90429.68103.380.9830.6460.68915.04732.60217.5693.138-25.376-25.242-21.07225.299-25.072-25.195-24.974
569.524103.32062.771101.89969.88427.9064.971.1550.6780.58011.76029.66216.2013.343-26.466-26.527-22.62124.064-26.489-26.536-26.426
671.563103.32066.071101.92173.96329.3069.222.1401.4381.68914.13732.73915.5732.418-27.581-28.038-23.35523.051-27.650-27.709-27.599
769.524103.32065.471101.96873.96329.7771.411.7691.5351.53417.95434.68018.2303.147-24.917-24.832-20.68926.138-24.539-24.538-24.668
871.563103.32066.271101.99672.94329.7668.751.3260.9450.88313.95229.12916.6083.931-25.890-25.801-22.52124.353-25.738-25.825-25.764
971.563103.32068.971101.99077.02228.9766.882.0041.7871.54816.88534.20918.1202.646-25.520-25.408-21.15925.961-25.353-25.567-25.470

Last rows

X_01X_02X_03X_04X_05X_06X_07X_08Y_01Y_02Y_03Y_04Y_05Y_06Y_07Y_08Y_09Y_10Y_11Y_12Y_13Y_14
3959767.485103.32062.771103.15467.84530.07104.721.4891.3691.30315.68734.08917.5863.107-25.927-25.836-21.61125.399-25.850-25.867-25.587
3959869.524103.32062.871103.14169.88432.68105.511.2990.6121.03217.95732.87016.8043.140-26.569-26.304-23.10224.660-26.259-26.410-26.365
3959966.465103.32061.371103.14166.82530.25114.360.9490.8910.76717.70630.87717.0902.547-26.652-26.807-22.18824.737-26.783-26.694-26.771
3960065.445103.32161.171103.12665.80528.89103.080.9980.5630.9118.87928.95716.4413.387-26.545-26.572-22.70524.084-26.618-26.677-26.530
3960164.425103.32063.871102.03768.86430.4099.991.5561.4181.32812.59832.67116.9492.996-26.106-26.281-22.35924.661-26.134-26.300-26.306
3960266.465103.32062.271103.15066.82530.2077.831.3821.2151.26310.87429.19416.5823.410-26.486-26.581-22.77224.261-26.491-26.584-26.580
3960366.465103.32162.771102.02166.82529.21102.251.4820.6061.0838.75929.85915.6593.406-27.308-27.203-24.67423.427-27.250-27.334-27.325
3960468.504103.32064.671103.14468.86429.96102.611.1171.1540.99313.15924.72016.8233.215-26.502-26.687-22.57724.301-26.388-26.425-26.601
3960566.465103.32063.671102.02567.84530.30112.600.8950.1870.4779.12326.41215.7574.216-26.760-26.634-24.06623.305-26.536-26.751-26.635
3960666.465103.32065.671102.00469.88430.16112.901.1470.3480.85210.42130.74516.7813.307-26.054-26.251-23.25724.450-26.224-26.256-26.093